ORIGINAL RESEARCH ARTICLEpublished: 11 November 2014

doi: 10.3389/fpsyg.2014.01292

It’s all in your head – how anticipating evaluation affects theprocessing of emotional trait adjectivesSebastian Schindler 1,2*, Martin Wegrzyn 1,2 , Inga Steppacher 1 and Johanna Kissler 1,2

1 Department of Psychology, Affective Neuropsychology, University of Bielefeld, Bielefeld, Germany2 Center of Excellence Cognitive Interaction Technology, University of Bielefeld, Bielefeld, Germany

Edited by:

Cornelia Herbert, University Clinic forPsychiatry and Psychotherapy,Germany

Reviewed by:

Nathaniel Delaney-Busch, TuftsUniversity, USAConstantino Méndez-Bértolo,Universidad Politécnica de Madrid,Spain

*Correspondence:

Sebastian Schindler, Departmentof Psychology, AffectiveNeuropsychology, University ofBielefeld, Bielefeld 33501, Germanye-mail: sebastian.schindler@uni-bielefeld.de

Language has an intrinsically evaluative and communicative function. Words can serveto describe emotional traits and states in others and communicate evaluations. Usingelectroencephalography (EEG), we investigate how the cerebral processing of emotionaltrait adjectives is modulated by their perceived communicative sender in anticipation ofan evaluation. 16 students were videotaped while they described themselves. They weretold that a stranger would evaluate their personality based on this recording by endorsingtrait adjectives. In a control condition a computer program supposedly randomly selectedthe adjectives. Actually, both conditions were random. A larger parietal N1 was foundfor adjectives in the supposedly human-generated condition. This indicates that morevisual attention is allocated to the presented adjectives when putatively interacting witha human. Between 400 and 700 ms a fronto-central main effect of emotion was found.Positive, and in tendency also negative adjectives, led to a larger late positive potential(LPP) compared to neutral adjectives. A centro-parietal interaction in the LPP-windowwas due to larger LPP amplitudes for negative compared to neutral adjectives withinthe ‘human sender’ condition. Larger LPP amplitudes are related to stimulus elaborationand memory consolidation. Participants responded more to emotional content particularlywhen presented in a meaningful ‘human’ context. This was first observed in the earlyposterior negativity window (210–260 ms). But the significant interaction between senderand emotion reached only trend-level on post hoc tests. Our results specify differentialeffects of even implied communicative partners on emotional language processing. Theyshow that anticipating evaluation by a communicative partner alone is sufficient to increasethe relevance of particularly emotional adjectives, given a seemingly realistic interactivesetting.

Keywords: EEG/ERP, emotion, language, social feedback, feedback anticipation, communicative context

INTRODUCTIONLanguage serves many different functions, ranging from thecommunication of facts and knowledge, to the communicationof socio-emotional evaluations. In fact, symbolic interaction-ism theory suggests, that language meaning is derived frominteraction with others (Blumer, 1969). This interaction is sup-posed to connect the identities of the communicating partners(Burke, 1980). For humans, communication using emotionallyrelevant language is of special interest (Barrett et al., 2007; Lieber-man et al., 2007). Accordingly, newspapers and advertisers oftenselect emotional words for their headlines, as their processingis prioritized (for a review see e.g., Zald, 2003; Kissler et al.,2006; Citron, 2012). However, influence of the social commu-nicative context on emotional word processing has not beenaddressed elaborately. The present study aims to do so by cre-ating an evaluative context and investigating whether processingof emotion-laden language differs in anticipation of personalityevaluation.

So far processing of emotional language has been mostly inves-tigated in the absence of communicative context. Neuroscienceresearch has shown that brain event-related potentials (ERPs)

differentiate between emotional and neutral contents during read-ing (Kissler et al., 2007) and in lexical (Schacht and Sommer,2009a,b), grammatical (Kissler et al., 2009) or evaluative decisiontasks (Naumann et al., 1997). Emotion effects are most consis-tently reflected in a larger early posterior negativity (EPN) arisingfrom about 200 ms, which is thought to reflect mechanisms of per-ceptual tagging and early attention (Kissler et al., 2007; Kissler andHerbert, 2013). A more pronounced late parietal positivity (LPP)from about 500 ms after word presentation, has been implicated inelaborative evaluation and memory processing of emotional words(Herbert et al., 2006, 2008; Kissler et al., 2006, 2009; Kanske andKotz, 2007; Hofmann et al., 2009; Schacht and Sommer, 2009b).

Previous work showed that establishing a self referential contextcan alter word processing at early (Fields and Kuperberg, 2012),as well as late processing stages (Watson et al., 2007; Shestyukand Deldin, 2010; Herbert et al., 2011a,b). This implies self-reference as one important source of plasticity in emotion wordprocessing.

According to symbolic interactionism, the discursive contextin which emotional language is embedded should likewise bean important source of plasticity in word processing. In social

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communication, participants have expectations about their com-municative partners and react to violations of these expectations(Burgoon et al., 1983, 2000). Therefore, establishing a socially rele-vant communicative context, rather than solely self-relevance, canbe expected to alter the way emotional language is processed.

Receiving feedback from another person regarding one’s ownpersonality represents a highly salient social context. For somepeople receiving feedback may even pose a social threat, sincehumans have a strong need to belong to a community (Baumeis-ter and Leary, 1995), seek approval by others (Izuma et al., 2010;Romero-Canyas et al., 2010), and try to avoid unfavorable evalu-ations (Leary, 1983; Carleton et al., 2011). Electrophysiologically,social threat has been shown to affect early visual ERP componentsand frontal EEG asymmetry (Crost et al., 2008; Trautmann-Lengsfeld and Herrmann, 2013; Baess and Prinz, 2014). Forexample, when participants due to group pressure agreed witha wrong answer option, the P1 was reduced compared to a percep-tually identical condition (Trautmann-Lengsfeld and Herrmann,2013). The P1 is one of the first evoked visual potentials. It reflectssensory registration and it is found to be larger for attended stim-uli (Mangun and Hillyard, 1991). Influence of social setting isalso reported for the N1 (Baess and Prinz, 2014). In a Go/Nogoparadigm, the N1 was found to be larger when both participantshad to react in Go trials (Baess and Prinz, 2014). The N1 is thoughtto be a marker of visual discrimination (Vogel and Luck, 2000) anddecreases with repetition (Carretié et al., 2003). Like the P1, the N1increases when stimuli are attended (Hillyard et al., 1998). P1/N1modulations have been occasionally reported for emotional stim-uli (Pourtois et al., 2004; Keil et al., 2007; Steinberg et al., 2013)and recent evidence shows that also social context may changevery early sensory processing.

These electron paramagnetic resonance (EPR) findings arecomplemented by fMRI results showing a regionally distinct pro-cessing of social feedback Social feedback has been shown toactivate reward system structures such as the medial prefrontalcortex and the ventral striatum as well as the anterior cingulatecortex, involved in pain processing (Somerville et al., 2006, 2010;Izuma et al., 2008, 2010; Davey et al., 2010; Eisenberger et al., 2011;Korn et al., 2012). Together EEG and fMRI data indicate that effectsof social feedback on brain physiology can be observed in artifi-cial laboratory conditions using highly temporally and spatiallyresolving imaging methods.

As humans constantly make predictions about the future(Koster-Hale and Saxe, 2013; Seth, 2013), even the anticipationof socially relevant feedback, for example delivered as gesturalapproval or disapproval (‘thumbs up’ or ‘thumbs down’). Thepresent study aims to do so by creating an evaluative context andinvestigating whether processing of emotion-laden language dif-fers in anticipation of personality evaluation. Produces distinctcerebral activities (Kohls et al., 2013). In this study, the avoidanceof social punishment and the anticipation of social reward led toenhanced activity in the ventral striatum and nucleus accumbens(Kohls et al., 2013). This indicates that both the fear of sociallyunfavorable evaluations and hope of acceptance are central humanmotives that modulate reward system biology.

The anticipation of socio-emotional language feedback,arguably the most common source of socially relevant feedback,

has not yet been investigated. However, there is information onthe effects of anticipatory anxiety on ERPs: research demonstratesunspecific sensitizing effects of threat of shock, reflected in morepositive-going early ERPs during threat-cue processing (Bublatzkyand Schupp, 2012). Trials signaling a possible electric shock, leadto a larger P1 and P2, as well as a larger parietal LPP comparedto trials signaling safety (Bublatzky et al., 2010; Bublatzky andSchupp, 2012). Moreover, anticipatory anxiety has been reportedto specifically accentuate the processing of emotional pictures,surprisingly leading to a larger EPN for positive pictures when tri-als are signaling a possible electric shock (Bublatzky et al., 2010).Using anticipation of speaking in public as a threat induction,a different study reported the arguably more intuitive finding ofaccentuated processing of negative stimuli: participants were toldthat they would supposedly held a speech in public after complet-ing a face perception task. Compared to a control condition thisled to a larger N170 and EPN for angry faces in the face perceptiontask (Wieser et al., 2010).

Anticipation of verbal social feedback likely involves a phaseof self-reflection, akin to self-referential processing, perhaps com-bined with anticipatory anxiety of negative feedback. The intensityof these processes may depend on both the message and the senderof the feedback. Existing studies of emotion word processing havefocused on the processing of single words in psycho-linguistictasks, devoid of social context. However, word meaning willchange depending on attributed sender characteristic and direc-tion of communication. In ecologically valid situations, alreadyan inferred psychological context or a psychological attributionto another individual may constitute presence or absence of aninteraction. For instance, feedback in the form of the adjective‘boring’ should be more important if another human is the puta-tive sender rather than a computer. Likewise, ‘boring’ may beregarded as more intense, when it is used to characterize oneself asa person rather than one’s teaching lesson. Similarly, an adjectivelike ‘cheap’ may be relatively neutral when describing an object,but becomes highly negative when it is used to characterize aperson.

Against this background, the present study examines the influ-ence of the putative sender on processing of negative, neutraland positive written adjectives in a social evaluative context. Par-ticipants were told that either an unknown other person wouldevaluate them based on his/her first impression, or a computerprogram would randomly highlight trait adjectives. In reality, bothconditions were random and perceptually identical. We expectedthat anticipation of feedback by another person would generallychange stimulus processing (sensitizing effects, Wieser et al., 2010or Bublatzky and Schupp, 2012) and investigated whether thisoccurs at early perceptual (P1, N1), mid-latency (EPN), or late(LPP) processing stages. Moreover, we examined valence-specificinteractions between feedback content and evaluative context(human, computer). Generally, in the context of being evaluatedby another person, negative, and positive trait adjectives can beexpected to induce larger P1, N1, EPN, or LPP amplitudes, reflect-ing fear of unfavorable evaluations and social rejection (Somervilleet al., 2006; Masten et al., 2009; Eisenberger et al., 2011) or hopeof acceptance by others (Izuma et al., 2010; Romero-Canyas et al.,2010; Simon et al., 2014).

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Against this background, we evaluate the sequence of early(P1, N1), mid-latency (EPN) and late visually evoked potentialsin response to adjectives presented as potential trait-feedback byanother human or a randomly acting computer.

MATERIALS AND METHODSPARTICIPANTSEighteen participants were recruited at the University of Bielefeld.They gave written informed consent according to the Declara-tion of Helsinki and received 10 Euros for participation. Thestudy was approved by the Ethics Committee of the Universityof Konstanz. Due to experimentation errors, two datasets hadto be excluded, leaving 16 participants for final analysis. Theresulting 16 participants (12 females) were 24.40 years on aver-age (SD = 0.66). All participants were native German speakers,had normal or corrected-to-normal visual acuity, and were right-handed. Twelve participants were undergraduate students; fourhad already received their Bachelor’s or Master’s degree. Screen-ings with the German version of the Beck Depression Inventoryand the State Trait Anxiety Inventory (Spielberger et al., 1999;Hautzinger et al., 2009), revealed no clinically relevant depres-sion (M = 4.12; SD = 4.54) or anxiety scores (M = 35.94;SD = 3.06).

STIMULIAdjectives were previously rated by 20 students in terms ofvalence and arousal using the Self-Assessment Manikins (Bradleyand Lang, 1994). Raters had been specifically instructed toconsider adjective valence and arousal in the context of beingdescribed by another person with this respective adjective.150 adjectives (60 negative, 30 neutral, 60 positive) wereselected and matched in their linguistic properties, such asword length, frequency, familiarity and regularity (see Table 1).Importantly, negative and positive adjectives differed only intheir valence. As there is a lack of truly neutral trait adjec-tives, neutral adjectives were allowed to differ from emo-tional adjectives on rated concreteness next to valence andarousal.

PROCEDUREParticipants were told that they would be rated by an unknownother person or would see ratings generated randomly by acomputer program. All subjects underwent both conditions.Sequence was counterbalanced across participants.

Upon arrival, participants were asked to describe themselves ina brief structured interview in front of a camera. They were toldthat their self-description was videotaped and would be shownto a second participant next door. The interview contained fourquestions encouraging the participant to talk about their strengthsand weaknesses, as well as giving a short biography overview.After the interview, participants filled out a demographic ques-tionnaire as well as BDI and STAI whilst the EEG was applied.To ensure face validity, a research assistant left the testing rooma couple of minutes ahead of the fictitious feedback, guidingan ‘unknown person’ to a laboratory room next to the testingroom.

Stimuli were presented within a desktop environment of a fic-titious program, allegedly allowing instant online communication(see Figure 1).

Network cables and changes of the fictitious software desktopimage showing a ‘neurobehavioral interactive systems’ environ-ment were implemented to enhance credibility. The 60 negative,30 neutral, and 60 positive adjectives were randomly presentedand feedback upon was randomly generated in both conditions.All adjectives were first presented in black. After a fixed (com-puter) or variable (human) time interval a color change indicatedthe feedback on a certain adjective. The presented results relateto the pre-feedback period, when all stimuli still appeared inblack. Half of all adjectives were endorsed, leading to 30 affir-mative negative, 30 neutral, and 30 affirmative positive decisions.While the presented feedback was randomly generated in bothconditions, twenty additionally inserted highly negative adjec-tives were defined to be always rejected in the ratings to furtherincrease credibility, since it would appear very unlikely for some-body to endorse extremely negative traits in a hardly knownstranger. These additional trials were excluded from further anal-ysis. The desktop environment and stimulus presentation were

Table 1 | Comparisons of negative, neutral and positive adjectives by one-way-anlysis of variances.

Variable Negative adjectives (n = 60) Neutral adjectives (n = 30) Positive adjectives (n = 60) F (2,147)

Valence 3.10a (0.84) 5.01b (0.32) 7.01c (0.90) 371.05***

Arousal 4.57a (0.85) 3.30b (0.66) 4.40a (0.85) 25.93***

Concreteness 3.24a (1.03) 5.07b (1.46) 3.16a (1.27) 28.10***

Word length 8.93 (2.65) 9.23 (2.94) 9.15 (2.48) 0.16

Word frequency (per million) 4.64 (8.56) 4.34 (6.26) 4.78 (8.05) 0.03

Familiarity (absolute) 21805.77 (39221.26) 18832.23 (48387.29) 19331.85 (42795.46) 0.07

Regularity (absolute) 261.58 (551.78) 165.97 (378.73) 239.06 (388.71) 0.44

Neighbors 3.45 (4.44) 2.53 (3.42) 3.78 (4.70) 0.83

Neighbors Levenshtein (absolute 6.13 (6.48) 4.93 (4.14) 6.60 (6.26) 0.76

***p ≤ 0.001. Standard deviations appear in parentheses below means; means in the same row sharing the same superscript letter do not differ significantly fromone another at p ≤ 0.05; means that do not share subscripts differ at p ≤ 0.05 based on LSD test post hoc comparisons.

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FIGURE 1 |Trial presentation using the fictitious interactive software. Each trial started with a presented trait adjective.

created using presentation1. In the ‘human’ condition between1500 and 2500 ms after adjective onset, color changes indicated adecision by the supposed interaction partner. This manipulationsimulated variable decision latencies in humans. The decision wascommunicated via color change (blue or purple) of the presentedadjective, indicating whether the respective adjective applied to theparticipant or not. Color–feedback assignments were counterbal-anced. In the computer condition, corresponding color changesalways occurred at 1500 ms, conveying the notion of constantmachine computing time. In both conditions color changes lastedfor 1000 ms, followed by a fixation cross for 1000–1500 ms. Aftertesting, participants responded to a questionnaire asking them torate their confidence in truly being judged by another person inthe ‘human’ condition, on a five point Likert-scale.

EEG RECORDING AND ANALYSESElectroencephalography signals were recorded from 128 BioSemiactive electrodes2. Four additional electrodes measured horizontaland vertical eye-movement. Recorded sampling rate was 2048 Hz.Pre-processing was done using SPM8 for EEG3. Although perhapsbest known as a toolbox for the analysis of functional magnetic res-onance data, SPM provides a unitary framework for the analysisof neuroscience data acquired with different technologies, includ-ing EEG and MEG using the same rationale (Penny and Henson,2007; Litvak et al., 2011). Offline, data were re-referenced to aver-age reference, downsampled to 250 Hz and butterworth band-passfiltered from 0.166 to 30 Hz. Recorded eye movements were sub-tracted from EEG data. Filtered data were segmented from 100 msbefore word onset until 1000 ms after word presentation. 100 mspreceding word onset were used for baseline-correction. Auto-matic artifact detection was used for trials exceeding a threshold of160 μV. Data were averaged, using the robust averaging algorithmof SPM8, excluding possible further artifacts. Overall, less than 1%

1http://www.neurobehavioralsystems.com2http://www.biosemi.com3http://www.fil.ion.ucl.ac.uk/spm/

of all electrodes were interpolated and on average 15.25% of alltrials were rejected, leaving on average 50.85 trials for emotionalwords and 25.43 trials for neutral words for each communica-tive sender. Artifact rejection rate did not differ between bothsenders [F(1,15) = 0.32, p = 0.58], nor between negative, neutraland positive content [F(2,30) = 0.26, p = 0.78]. There was alsono interaction between sender and emotional content regardingartifact rejection rate [F(2,30) = 0.09, p = 0.91].

STATISTICAL ANALYSESElectroencephalography scalp-data were statistically analyzed withEMEGS4, (Peyk et al., 2011). Two (sender: human versus com-puter) by three (emotion: positive, negative, neutral) repeatedmeasure ANOVAs were set-up to investigate main effects of thecommunicative sender, emotion and their interaction in timewindows and electrode clusters of interest. If Mauchly’s Testsof Sphericity yielded significance, degrees of freedom were cor-rected according to Greenhouse-Geisser as Greenhouse-Geisser ε’swere below 0.75. Partial eta-squared (partial η2) was estimated todescribe effect sizes, where η2 = 0.02 describes a small, η2 = 0.13a medium and η2 = 0.26 a large effect (Cohen, 1988). Time win-dows were segmented from 50 to 100 ms to investigate P1 and from100 to 150 ms to investigate N1 effects (Bublatzky and Schupp,2012; Fields and Kuperberg, 2012), from 210 to 260 ms to inves-tigate EPN effects (Kissler et al., 2007) and from 400 to 700 ms toinvestigate LPP effects (Schupp et al., 2004; Bublatzky and Schupp,2012).

For the P1 a fronto-central cluster was investigated (13 elec-trodes: FFC1h, FFCz, FFC2h, FC1h, FCz, FC2h, FCC1h, FCC2h,C1, C1h, Cz, C2h, C2), while for the N1 time window a parietalcluster of nineteen electrodes was examined (CCPz, CP1h, CPz,CP2h, CPP1, CPz, CPP2, P1, Pz, P2, PPO1, PPOz, PPO2, PO1,POz, PO2, POO1, POOz, POO2; see Figure 2). For the EPN timewindow, two symmetrical occipital clusters of eleven electrodeseach were examined (left: I1, OI1, O1, PO9, PO9h, PO7, P9, P9h,

4http://www.emegs.org/

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FIGURE 2 | Selected electrode clusters for the early time windows.

Selected electrodes are highlighted by color.

FIGURE 3 | Selected electrode clusters for the late time window.

Selected electrodes are highlighted by color.

P7, TP9h, TP7; right: I2, OI2, O2, PO10, PO10h, PO8, P10, P10h,P8, TP10h, TP8).

Late positive potential topographies have found to vary, withsome authors reporting more parietal others more fronto-centraldistributions, or even both in one study (Kissler et al., 2009). Sincethe present data revealed conspicuous differences both at fronto-central and at parietal sites two electrode groups of interest wereanalyzed for this component. For the LPP time window a fronto-central cluster (14 electrodes: F1h, Fz, F2h, FFC1h, FFCz, FFC2h,FC1h, FCz, FC2h, FCC1h, FCC2h, C1, Cz, C2) and a centro-parietal cluster were investigated (13 electrodes: CCP1h, CCPz,CCP2h, CP1, CP1h, CPz, CP2h, CP2, CPPz, P1, Pz, P2, PPOz; seeFigure 3).

RESULTSQUESTIONNAIRE DATAAfter debriefing, two participants stated that they were stronglyconvinced that they had been rated by another person in the‘human’ evaluation condition, six participants said they quiteconvinced, four participants somewhat convinced, and two

participants said they were little convinced. Mean credibility was3.4 (SD = 1.02) on a Liktert-scale ranging from one to five.

P1No significant main effects of sender F(1,15) = 0.18, p = 0.68,emotion F(2,30) = 0.12, p = 0.89, partial η2 = 0.05 and no inter-action F(2,30) = 0.52, p = 0.59, partial η2 = 0.05 was observedover fronto-central regions.

N1A significant main effect was observed for the communicativesender over the parietal sensor cluster between 100 and 150 msF(1,15) = 7.51, p < 0.05, partial η2 = 0.33 (see Figure 4). Theputative ‘human sender’ evoked a significantly larger N1 comparedto the computer sender. There was no main effect of emotionF(2,30) = 0.83, p = 0.44, partial η2 = 0.05 and no interactionbetween sender and emotion F(2,30) = 0.27, p = 0.76, partialη2 = 0.02.

EPNA significant interaction between sender and emotion wasobserved over occipital sensors during the EPN F(2,30) = 3.95,p < 0.05, partial η2 = 0.21. This interaction was based on a largerEPN for emotional adjectives within the ‘human sender’ com-pared to a larger EPN for neutral adjectives within the computersender. However, within the ‘human sender’ post hoc comparisonsshowed only a trend for a larger negativity for positive compared toneutral adjectives (p = 0.06) and no differences between negativeand neutral words (p = 0.55). Within the ‘computer sender’ neu-tral words elicited a trend- level larger EPN compared to negativewords (p = 0.08) but not compared to positive words (p = 0.28).

There were no main effects of the sender F(1,15) = 0.79,p = 0.38, partial η2 = 0.05 or of the emotional contentF(2,30) = 0.91, p = 0.41, partial η2 = 0.06 in the EPN timewindow.

LPPOver the fronto-central electrode cluster, a significant main effectfor emotion was observed F(2,30) = 3.49, p < 0.05, partialη2 = 0.19 (see Figure 5). Post hoc comparisons revealed, that pos-itive adjectives elicited a larger LPP compared to neutral adjectives(p < 0.05), while negative compared to neutral adjectives elicited alarger amplitude only in tendency (p = 0.13). Positive and negativewords did not differ from each other (p = 0.59). Over the fronto-central cluster there was no main effect of sender F(1,15) = 0.30,p = 0.59, partial η2 = 0.02 nor an interaction between sender andemotion F(1.27,19.11) = 0.20, p < 0.83, partial η2 = 0.01.

Over the centro-parietal electrode group a significant interac-tion between the communicative sender and emotional contentwas found F(2,30) = 3.46, p < 0.05, partial η2 = 0.19 (seeFigure 6). Post hoc comparison showed, that within the ‘humansender’ negative words elicited a significantly larger LPP com-pared to neutral adjectives (p < 0.01), while the somewhatlarger LPP for positive words compared to neutral words didnot reach significance (p = 0.15). Negative and positive wordsdid not differ from each other (p = 0.17). Within the ‘computersender’ no differences were found in any comparison (ps > 0.49).

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Over the centro-parietal cluster there were no main effects ofsender F(1,15) = 0.23, p = 0.64, partial η2 = 0.02 or emotionF(2,30) = 1.31, p = 0.29, partial η2 = 0.08.

DISCUSSIONWe hypothesized that anticipating an evaluative decision from ahuman sender would lead to altered processing of trait adjectivesby the recipient. A ‘computer sender’ was introduced as a source ofrandom evaluation to provide a maximal contrast between bothconditions, while maintaining identical perceptual input. The datareveal effects of sender and emotion as well as interactions. For the‘human sender,’ a significantly larger N1 between 100 and 150 msafter adjective onset was detected over parietal areas. Starting withthe EPN,effects of emotion interacted with perceived sender and inthe LPP window, both main effects of sender and emotion as wellas their interaction was observed. In the following, we will discussthese findings against the background of the current literature.

An early-onset effect of the ‘human sender’ condition, alreadyin the N1 window, is in line with earlier findings of rapid effects

of self-relevance (Fields and Kuperberg, 2012), as well as withsensitizing effects of social threat (Wieser et al., 2010). Within thebroader context of the ERP literature, N1 effects suggest more tonicattention orienting toward stimuli supposedly sent by a human.Tonic effects of attention deployment have first been observed byEason et al. (1969), who also were the first to demonstrate similareffects of volitional attention and threat of an electric shock onvisual stimulus processing.

A main effect of emotion was observed in the LPP time windowover a fronto-central electrode cluster. Here, positive and in ten-dency also negative words elicited a larger positivity comparedto neutral words. Descriptively, ERPs differed earlier betweenemotional and neutral adjectives (see Figure 6), but interactioneffects may have canceled out by stronger main effects of emotion.Brain topographies in the LPP time window differed somewhatbetween negative and positive adjectives. For the emotion maineffect over the fronto-central cluster, a larger positivity was onlyfound for positive adjectives, while for the interaction over thecentro-parietal cluster the post hoc comparison was only significant

FIGURE 4 | Results for the main effect of communicative source at the N1. (A) Difference topographies. Blue color indicates more negativity and red colormore positivity in the ‘human sender’ condition. (B) Selected electrodes CPPz, displaying the time course over parietal sites.

FIGURE 5 | Main effect for the emotional content in the late positive

potential time window. (A) Head Models for the post hoc comparisonswithin the respective emotion. Blue color indicates more negativity and red

color more positivity for the respective difference. (B) Selected electrode FCzshowing the enhanced positivity for positive and as a trend also for negativeadjectives compared to neutral adjectives.

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FIGURE 6 | Interaction between communicative sender and emotional

content in the late positive potential time window. (A) Head Models forthe post hoc comparisons within the respective communicative sender. Bluecolor indicates more negativity and red color more positivity for the

respective difference. (B) Selected electrode CCPz showing the largerpositivity for negative compared to neutral adjectives within the ‘humansender’ and small differences between emotional and neutral adjectiveswithin the ‘computer sender.’

for negative adjectives (see Figures 5 and 6). LPP topographyvariations have been found to vary in the same study (Kissleret al., 2009), but not such valence dependent variability. It maybe hypothesized that both arousal dependent and valence spe-cific processing, relying on partly differing generator structuresexist in the LPP time window regarding positive and negativeadjectives.

Processing of positive and negative adjectives was expectedto differ between the social evaluation and the feedback con-dition as reflected in an interaction between emotional contentand communicative sender. Early interactions – between 210 and260 ms – were found over the occipital region. However, posthoc comparisons revealed no clearly significant differences withinthe respective senders. Descriptively, within the ‘human sender’there was a larger EPN for emotional words, while for the ‘com-puter sender’ the EPN was somewhat more pronounced for neutralwords. Such early (210–260 ms) valence-specific modulations arerelatively rare, previous work reported mainly arousal effects inthis time window. However, Fields and Kuperberg (2012) reportedvery early effects of an established self-referential context on wordprocessing. Therefore, it may be specific to the present experi-mental setting and may be further enhanced by the presently usedblocked design.

Between 400 and 700 ms a larger positivity for negative adjec-tives compared to neutral adjectives was observed over parietalsites within the ‘human sender.’ The comparison between positiveand neutral adjectives, while qualitatively similar did not reachsignificance. For the ‘computer sender’ no differential processing

of negative, neutral and positive adjectives could be observed overcentral sites and in late time windows. The interaction effects indi-cate that the also reported LPP emotion main effect may be drivenpartly by the ‘human sender’ (see Figures 5 and 6). Such emo-tion main effects in the LPP time window have been reportedpreviously in typical psycho-linguistic experiments that did notexplicitly manipulate context (Herbert et al., 2006, 2008; Kissleret al., 2006, 2009; Kanske and Kotz, 2007; Hofmann et al., 2009;Schacht and Sommer, 2009b). However, as some studies do notfind late emotion effects (Rellecke et al., 2011) it may be helpfulto consider the communicative context. The present data suggestthat emotional differences largely derive from the adopted com-municative context or are at least amplified by it. By contrasting ameaningless and a meaningful passive visual word processing con-dition the differentiation between emotional and neutral wordsis heightened. Generally, the LPP is associated with elaborativeprocessing and larger LPPs have been shown to predict better sub-sequent memory (Dolcos and Cabeza, 2002), one might speculatethat contextual factors can determine whether emotional materialis only transiently attended at early processing stages or elaboratedon and commited to memory.

An interaction of emotion with the anticipatory context isin line with findings from shock-threatening (Bublatzky et al.,2010) or from socially threatening situations (Wieser et al., 2010).However, this is the first study which investigated anticipatoryeffects in a socially relevant communicative context, as extantstudies focus on processing of the feedback decision, typicallyalso using fMRI (Somerville et al., 2006, 2010; Izuma et al., 2008,

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2010; Davey et al., 2010; Korn et al., 2012). Due to the highertime resolution of the EEG, we were able to investigate how theanticipated feedback on trait adjectives changes in response tothe putative sender identity in distinct processing phases. Here,in addition to sensitizing effects due to threat or self-relevance(Bublatzky et al., 2010; Bublatzky and Schupp, 2012; Fields andKuperberg, 2012) the anticipation of human-generated evalua-tions led to differential processing of negative adjectives, whichwas pronounced at later stages. Descriptively, larger differencesbetween emotional and neutral words within the ‘human sender’compared to the ‘computer sender’ condition could be observedalready at the EPN. Emotional words may initially capture moreattention resources, but ongoing processing led to a pronounceddifferentiation between emotional and neutral words, reflected inthe enhanced central positivity in the LPP time window for emo-tional words. As sensitizing effects of threat have previously beenfound to accentuate selectively positive (Bublatzky et al., 2010) ornegative (Wieser et al., 2010) stimulus processing, in this socialcommunicative setting more complex motives may play a role.This could be explained by considerations that humans, in theabsence of conflicting evidence, tend to view themselves posi-tively (self-positivity bias), but also fear unfavorable evaluation(Leary, 1983; Somerville et al., 2006; Masten et al., 2009; Car-leton et al., 2011; Eisenberger et al., 2011) and seek approval andacceptance by others (Izuma et al., 2010; Romero-Canyas et al.,2010). Perhaps these different motifs play a role at distinct pro-cessing stages, maybe even by partly distinct cortical generatorstructures.

Overall, we cannot exclude that some relevant effects remainedundetected, due to the limited number of trials in each cell result-ing in limited power. Still, we observed considerable main andinteraction effects, suggesting that the study design was able todetect differences between the two putative senders and their effecton processing of emotional trait adjectives during feedback antic-ipation. Furthermore, credibility ratings for the ‘human sender’condition indicate successful experimental manipulation of therespective conditions. Self-reported credibility was not signifi-cantly correlated with N1 sender differences (two-tailed Pearsoncorrelation r = −0.11, p = 0.70, N = 16; two-tailed Spearmancorrelation rs = −0.31, p = 0.25, N = 16), making it unlikely thatsender main effects could be explained entirely by credibility. Alimitation of the presented study may be the generation of ade-quate neutral trait adjectives. Although all adjectives were tightlymatched for all linguistic characteristics, neutral adjectives differedfrom negative and positive adjectives in arousal and in concrete-ness. Still, this could neither account for sender differences nor forthe valence-specific accentuation of positive or negative contents.Remarkably, the results suggests that in spite of identical perceptualinput, the processing of a message, as reflected by electro-corticalactivity, changes as a function of the perceived communicativesignificance. Thus, subjective meaning seems not only to derivefrom real, but crucially also from supposed interaction with others,connecting not only real but even imaginary identities of commu-nicating partners. In the current study the ‘human sender’ wasthe only sender able to give meaningful feedback. It would beinteresting to compare a putative ‘human sender’ with a ‘computersender’ able to give personality feedback, to specify unique effects

of ‘humanness’ in contrast to only skill attributions. In general thisparadigm suggests many different possible sender manipulationswhich may contribute to our understanding of context influenceson (emotional) language processing. Further, it may be worth toknow if such very early visual modulations can be replicated inexperiments not using blocked within-subject designs.

CONCLUSIONSummarizing the main results, we found an amplified N1 indicat-ing, regardless of content, the allocation of more early attentionalresources to the trait adjectives if the putative sender was anotherhuman rather than a randomly operating computer. These dif-ferences were present already in anticipation of a decision andusing the identical visual input across conditions. In the EPNwindow, an interaction suggested that emotional adjectives in thehuman sender condition were processed more intensely, but posthoc tests did not reveal clearly significant differences, preclud-ing firm conclusions. Emotional adjectives led to a larger LPP.This interacted with sender: the LPP was particularly large whenevaluations were expected from a human sender. This suggeststhat at early processing stages attention is allocated to all stimuli,indiscriminate of emotional content and only after (or simulta-neously with) extraction of content at an evaluative processingstage selective amplification of emotional content in the humansender condition occurs. These findings indicate that imaginarysocial context has a large impact on language processing withinthe larger framework of symbolic interactionism.

AUTHOR CONTRIBUTIONSSebastian Schindler and Johanna Kissler contributed to the studydesign. Sebastian Schindler, Martin Wegrzyn, and Inga Steppachercarried out participant testing, Sebastian Schindler and JohannaKissler performed statistical analysis, Sebastian Schindler draftedthe manuscript under the supervision of Johanna Kissler. Mar-tin Wegrzyn and Inga Steppacher helped to draft and revise themanuscript. All authors read and approved the final manuscript.Sebastian Schindler revised the manuscript under supervision ofJohanna Kissler.

ACKNOWLEDGMENTSThis research was founded by the Deutsche Forschungsgemein-schaft, DFG KI1283/4-1 and by the DFG, Cluster of Excellence277 “Cognitive Interaction Technology.” We acknowledge supportfor the Article Processing Charge by the Deutsche Forschungsge-meinschaft and the Open Access Publication Funds of BielefeldUniversity Library. We would like to thank all participantscontributing to this study.

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Conflict of Interest Statement: The authors declared that they had no conflict ofinterest with respect to their authorship or the publication of this article.

Received: 28 July 2014; accepted: 24 October 2014; published online: 11 November2014.Citation: Schindler S, Wegrzyn M, Steppacher I, and Kissler J (2014) It’s all in yourhead – how anticipating evaluation affects the processing of emotional trait adjectives.Front. Psychol. 5:1292. doi: 10.3389/fpsyg.2014.01292This article was submitted to Language Sciences, a section of the journal Frontiers inPsychology.Copyright © 2014 Schindler, Wegrzyn, Steppacher and Kissler. This is an open-accessarticle distributed under the terms of the Creative Commons Attribution License(CC BY). The use, distribution or reproduction in other forums is permitted, pro-vided the original author(s) or licensor are credited and that the original publication inthis journal is cited, in accordance with accepted academic practice. No use, distributionor reproduction is permitted which does not comply with these terms.

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